Patient preferences for the delivery of bad news - the experience of a UK Cancer Centre.

The primary aim of this study was to assess how patients would prefer to be given their cancer diagnosis in a typical UK cancer centre. Two hundred and forty-four patients attending the oncology outpatient department at the Leicester Royal Infirmary, UK, were recruited. Patients were invited to complete the Measure of Patients' Preferences questionnaire, write comments on their own experience of the breaking bad news consultation and choose their preferred role in decision making. Over 90% of questionnaires were completed. Patients rated the items addressing the message content of the consultation as more important than the facilitative or the supportive aspects. Over 80% of patients wrote a detailed account of their experiences, of which 60% were satisfied with the consultation. Most of the patients who were dissatisfied commented on the unsympathetic or pessimistic manner of the doctor. The majority of patients wanted a collaborative role in decision making. Regarding the cancer diagnosis, the majority of patients have information needs, want to be involved in treatment decisions and know their prognosis. The difficulty for physicians is how to meet individual information needs, give hope, but not deliver unrealistic expectations.

Communication training in oncology: results of intensive communication workshops for Italian oncologists.

The primary aim of this study was to evaluate the efficacy of a 3-day communication course model for senior Italian oncologists. The course, initially designed for US oncologists, was modified to address specific educational areas expected to be relevant to the targeted participants. Five 3-day intensive communication courses were held for oncologists from different geographical regions in Italy. The courses included formal lectures, small group work, role play and interviews with simulated patients. Participants completed questionnaires before and after the 3-day workshop. An improvement in self-efficacy, knowledge of communication skills, favourable changes in attitudes towards disclosure of medical information and assessing patients' concerns and fears were demonstrated at the end of the course. The course was feasible and succeeded in improving parameters associated with effective communication behaviours.

Acute and subacute effects of a synthetic kisspeptin analog, C6, on serum concentrations of luteinizing hormone, follicle stimulating hormone, and testosterone in prepubertal bull calves.

Kisspeptin (KP) is a neuropeptide integral in regulating puberty and gonadotropin releasing hormone. Compound 6 (C6), a KP analog, is more potent in vitro, has a longer half-life, and may have greater therapeutic applications than KP. To determine the acute and subacute effects of KP and C6 on serum concentrations of luteinizing hormone (LH), follicle stimulating hormones (FSH), and testosterone (T), prepubertal bull calves [12.1 ±â€¯1.1 (SD) weeks of age; 91.2 ±â€¯10.8 kg BW] were assigned to one of three treatment groups [Saline (n = 4), KP (n = 4; 20 nmoles), or C6 (n = 4; 20 nmoles). Treatments were administered intramuscularly once daily for four consecutive days. Blood samples were collected every 15 min for 6 h immediately following treatment administration on Day 1 (acute) and Day 4 (subacute). Serum concentrations of LH, FSH, and T were determined by radioimmunoassay. For each day, effects of treatment, time, and interactions on LH and FSH concentrations and pulse parameters were analyzed using procedures for repeated measures with JMP Software (SAS Inst. Inc., Cary, NC). There was a treatment × time interaction during Day 1 (P < 0.0001) and Day 4 (P = 0.02) such that LH concentrations were greatest following administration of C6 (albeit diminished during Day 4). Number of LH pulses were least (P = 0.02) and LH nadirs were highest (P = 0.04) following administration of C6 (P = 0.02). There was no effect of treatment (P = 0.95) or treatment × time interaction (P = 0.10) on serum FSH concentrations during Day 1. During Day 4 FSH concentrations (P = 0.02) and number of FSH pulses (P = 0.02) were least following administration of C6. There was no effect of treatment (P = 0.33), time (P = 0.19) or treatment × time interaction (P = 0.44) on T concentrations. In conclusion, acute and subacute C6 increased LH concentrations and subacute C6 decreased FSH concentrations and pulse parameters. Despite suppression of FSH with subacute daily administration of C6, altered frequency and timing of treatment with KP analogs may have application to affect the onset of puberty in livestock.

Breaking bad news about cancer: patients' preferences for communication.

PURPOSE: The goal of this study was to assess patients' preferences regarding the way in which physicians deliver news about their cancer diagnosis and management. PATIENTS AND METHODS: A sample of 351 patients with a variety of cancers completed a measure assessing their preferences for how they would like to be told news about their cancer. Patients rated characteristics of the context and content of the conversation as well as physician characteristics. RESULTS: Factor analysis indicated that patients' preferences for how they would like to be told news regarding their cancer can be grouped into the following three categories: (1) content (what and how much information is told); (2) facilitation (setting and context variables); and (3) support (emotional support during the interaction). Women (P =.02) and patients with higher education (P =.05) had significantly higher scores on the Content scale, women (P =.02) had higher scores on the Support scale, and younger patients (P =.001) and those with more education (P =.02) had higher scores on the Message Facilitation scale. Medical variables were not associated with patients' ratings of the importance of the three subscales. CONCLUSION: Patients rated items addressing the message content as most important, though the supportive and facilitative dimensions were also rated highly. Understanding what is important to patients when told news about their cancer provides valuable information that may help refine how this challenging task is best performed.

Solid cancers after bone marrow transplantation.

PURPOSE: To evaluate the incidence and associated risk factors of solid cancers after bone marrow transplantation (BMT). PATIENTS AND METHODS: We analyzed 2,129 patients who had undergone BMT for hematologic malignancies at the City of Hope National Medical Center between 1976 and 1998. A retrospective cohort and nested case-control study design were used to evaluate the role of pretransplantation therapeutic exposures and transplant conditioning regimens. RESULTS: Twenty-nine patients developed solid cancers after BMT, which represents a two-fold increase in risk compared with a comparable normal population. The estimated cumulative probability (+/- SE) for development of a solid cancer was 6.1% +/- 1.6% at 10 years. The risk was significantly elevated for liver cancer (standardized incidence ratio [SIR], 27.7; 95% confidence interval [CI], 1.9 to 57.3), cancer of the oral cavity (SIR, 17.4; 95% CI, 6.3 to 34.1), and cervical cancer (SIR, 13.3; 95% CI, 3.5 to 29.6). Each of the two patients with liver cancer had a history of chronic hepatitis C infection. All six patients with squamous cell carcinoma of the skin had chronic graft-versus-host disease. The risk was significantly higher for survivors who were younger than 34 years of age at time of BMT (SIR, 5.3; 95% CI, 2.7 to 8.6). Cancers of the thyroid gland, liver, and oral cavity occurred primarily among patients who received total-body irradiation. CONCLUSION: The risk of radiation-associated solid tumor development after BMT is likely to increase with longer follow-up. This underscores the importance of close monitoring of patients who undergo BMT.

Myoelectric control of prostheses.

The development of myoelectric control systems for powered limb prostheses has advanced rapidly in recent years. The main thrusts in this development have been in realizing self-contained prostheses and in realizing better prostheses control through improvements in the myoelectric signal processing techniques. This review considers the latter of these two areas. It first presents an historical look at myoelectric signal processing and identifies the problems. It then presents a general look at the myoelectric signal and those characteristics which give rise to these problems. A review of the literature related to various control strategies and signal processing techniques to overcome these problems is given. Finally, future trends to be expected in this area are discussed.

Quality of life in patients with malignant melanoma participating in a phase I trial of an autologous tumour-derived vaccine.

Quality of life (QOL) plays an increasingly important role in the decision-making process and the ultimate acceptability of particular treatments for patients. We prospectively examined QOL in patients with advanced melanoma treated with surgery followed by heat-shock protein peptide complex 96 (HSPPC-96) vaccine, an active, patient-specific immunotherapy. QOL (the RAND 36-Item Health Survey; SF-36) and cancer-related intrusive thoughts (Impact of Event Scale; IES) were measured at the start of treatment, 3 weeks later on the final day of treatment, and at follow-up 1 month later in 30 patients with stage III or IV malignant melanoma. Mixed model analyses revealed no significant change over time in the SF-36 Physical or Mental Component Summary scores, or the IES scores. In comparisons with other populations, at the 1 month follow-up assessment, melanoma patients reported similar QOL to patients with metastatic renal cell carcinoma who received the same treatment, significantly worse QOL on the physical dimensions and similar QOL on the psychosocial and emotional dimensions compared with the general population, similar QOL to patients with type II diabetes, and significantly better QOL on all three dimensions than patients with congestive heart failure. There was also a significant negative association between IES scores at baseline and mental health scores at each time point ( < 0.002 for all). QOL remained stable during treatment with the HSPPC-96 vaccine. In addition, patients who reported high levels of cancer-related intrusive thoughts at the start of treatment reported worse mental health at the end of the treatment.

Single motor unit myoelectric signal analysis with nonstationary data.

The information content of the myoelectric signal (MES) is commonly revealed by statistical measures in the time or frequency domain. Empirical analyses of the MES from a single motor unit have generally assumed that features are invariant with time. Theoretical and experimental work has been done to demonstrate how nonstationary behavior in the discharge statistics of a motor neuron may affect estimates of features extracted from the motor unit's contribution to the MES. Specifically, it has been shown that nonstationary behavior can markedly influence estimates of features describing motor neuron firing behavior and consequently, the low-frequency portion of the MES power spectral density. These results may help to explain the discrepancies in the literature which report empirical models of motor neuron firing statistics.

Multireference adaptive noise cancellation applied to somatosensory evoked potentials.

Somatosensory Evoked Potentials (SEP's) contain information that is useful in diagnosing various physiological disorders. However, surface measurements of these potentials suffer from very poor Signal-to-Noise ratio (SNR) resulting in imperceptible SEP waveforms. This factor motivates the employment of dedicated signal processing techniques to improve the quality of the waveform. The objective of this research work is to improve the SNR of SEP by eliminating the predominant myoelectric interference. The strategy followed to achieve this goal is to process the SEP signal by MultiReference Adaptive Noise Cancellation (MRANC). A theoretical model for the MRANC is presented and its performance under the influence of various factors is investigated and compared with other signal processing techniques. The performance of the MRANC is then evaluated by processing simulated and in vivo SEP data. It is found that the MRANC gives a significant improvement in the SNR of the SEP.

A beamformer for the acquisition of evoked potentials.

Evoked potentials (EP) contain information about various physiological parameters and the estimation and detection of these signals can aid in the diagnosis of many pathological conditions. However, the signal-to-noise ratio (SNR) for EP measurement is often very low, and thus signal processing techniques must be employed to enhance the SNR. A delay and sum beamformer acquisition system has the potential for significant SNR improvement in EP measurements. In this communication it is shown that an electrode array acquisition system implements a uniform coherent delay and sum beamformer. The performance of the beamformer is characterized in terms of the number of electrodes, and cross-channel correlation. When compared to conventional ensemble averaging, the beamformer reduces the number of response repetitions required to achieve a given SNR by a factor which approaches the number of channels in the acquisition system.

Operator error in a level coded myoelectric control channel.

Two forms of error exist in the level coded myoelectric control channel: system error and operator error. Currently in level coded (3-state) myoelectric prosthesis, target and switching level settings are optimized for the presence of system error only. In this study, system error was minimized in order to examine operator error. The magnitude of the operator error was found to exceed the magnitude of the experimental system error as well as the system error associated with a typical prosthesis control unit. These findings suggest that operator error should be considered when optimizing target levels and decision boundaries for level coded myoelectric prosthesis controllers. Since the operator response was estimated to be normally distributed, it is described by its mean and standard deviation. This information can be used to determine the desired optimal settings.

Adaptive stimulus artifact reduction in noncortical somatosensory evoked potential studies.

Somatosensory evoked potentials (SEP's) are an important class of bioelectric signals which contain clinically valuable information. The surface measurements of these potentials are often contaminated by a stimulus evoked artifact. The stimulus artifact (SA), depending upon the stimulator and measurement system characteristics, may obscure some of the information carried by the SEP's. Conventional methods for SA reduction employ hardware-based circuits which attempt to eliminate the SA by blanking the input during SA period. However, there is a danger of losing some of the important SEP information, especially if the stimulating and recording electrodes are close together. In this paper, we apply both linear and nonlinear adaptive filtering techniques to the problem of SA reduction. Nonlinear adaptive filters (NAF's) based on truncated second-order Volterra series expansion are discussed and their applicability to SA cancellation is explored through processing both simulated and in vivo SEP data. The performances of the NAF and the finite impulse response (FIR) linear adaptive filter (LAF) are compared by processing experimental SEP data collected from different recording sites. Due to the inherent nonlinearities in the generation of the SA, the NAF is shown to achieve significantly better SA cancellation compared to the LAF.

A wavelet-based continuous classification scheme for multifunction myoelectric control.

This work represents an ongoing investigation of dexterous and natural control of powered upper limbs using the myoelectric signal. When approached as a pattern recognition problem, the success of a myoelectric control scheme depends largely on the classification accuracy. A novel approach is described that demonstrates greater accuracy than in previous work. Fundamental to the success of this method is the use of a wavelet-based feature set, reduced in dimension by principal components analysis. Further, it is shown that four channels of myoelectric data greatly improve the classification accuracy, as compared to one or two channels. It is demonstrated that exceptionally accurate performance is possible using the steady-state myoelectric signal. Exploiting these successes, a robust online classifier is constructed, which produces class decisions on a continuous stream of data. Although in its preliminary stages of development, this scheme promises a more natural and efficient means of myoelectric control than one based on discrete, transient bursts of activity.

Nonlinear adaptive filtering of stimulus artifact.

Noninvasive measurements of somatosensory evoked potentials have both clinical and research applications. The electrical artifact which results from the stimulus is an interference which can distort the evoked signal, and introduce errors in response onset timing estimation. Given that this interference is synchronous with the evoked signal, it cannot be reduced by the conventional technique of ensemble averaging. The technique of adaptive noise cancelling has potential in this regard however, and has been used effectively in other similar problems. An adaptive noise cancelling filter which uses a neural network as the adaptive element is investigated in this application. The filter is implemented and performance determined in the cancelling of artifact for in vivo measurements on the median nerve. A technique of segmented neural network training is proposed in which the network is trained on that segment of the record time window which does not contain the evoked signal. The neural network is found to generalize well from this training to include the segment of the window containing the evoked signal. Both quantitative and qualitative measures show that significant stimulus artifact reduction is achieved.

The application of neural networks to myoelectric signal analysis: a preliminary study.

Two neural network implementations are applied to myoelectric signal (MES) analysis tasks. The motivation behind this research is to explore more reliable methods of deriving control for multidegree of freedom arm prostheses. A discrete Hopfield network is used to calculate the time series parameters for a moving average MES model. It is demonstrated that the Hopfield network is capable of generating the same time series parameters as those produced by the conventional sequential least squares (SLS) algorithm. Furthermore, it can be extended to applications utilizing larger amounts of data, and possibly to higher order time series models, without significant degradation in computational efficiency. The second neural network implementation involves using a two-layer perceptron for classifying a single site MES based on two features, specifically the first time series parameter, and the signal power. Using these features, the perceptron is trained to distinguish between four separate arm functions. The two-dimensional decision boundaries used by the perceptron classifier are delineated. It is also demonstrated that the perceptron is able to rapidly compensate for variations when new data are incorporated into the training set. This adaptive quality suggests that perceptrons may provide a useful tool for future MES analysis.

The short-time Fourier transform and muscle fatigue assessment in dynamic contractions.

The mean frequency of the power spectrum of an electromyographic signal is an accepted index for monitoring fatigue in static contractions. There is however, indication that it may be a useful index even in dynamic contractions in which muscle length and/or force may vary. The objective of this investigation was to explore this possibility. An examination of the effects of amplitude modulation on modeled electromyographic signals revealed that changes in variance created in this way do not sufficiently affect characteristic frequency data to obscure a trend with fatigue. This validated the contention that not all non-stationarities in signals necessarily manifest in power spectral parameters. While an investigation of the nature and effects of non-stationarities in real electromyographic signals produced from dynamic contractions indicated that a more complex model is warranted, the results also indicated that averaging associated with estimating spectral parameters with the short-time Fourier transform can control the effects of the more complex non-stationarities. Finally, a fatigue test involving dynamic contractions at a force level under 30% of peak voluntary dynamic range, validated that it was possible to track fatigue in dynamic contractions using a traditional short-time Fourier transform methodology.

Neural network classification of myoelectric signal for prosthesis control.

An alternate approach to deriving control for multidegree of freedom prosthetic arms is considered. By analyzing a single-channel myoelectric signal (MES), we can extract information that can be used to identify different contraction patterns in the upper arm. These contraction patterns are generated by subjects without previous training and are naturally associated with specific functions. Using a set of normalized MES spectral features, we can identify contraction patterns for four arm functions, specifically extension and flexion of the elbow and pronation and supination of the forearm. Performing identification independent of signal power is advantageous because this can then be used as a means for deriving proportional rate control for a prosthesis. An artificial neural network implementation is applied in the classification task. By using three single-layer perceptron networks, the MES is classified, with the spectral representations as input features. Trials performed on five subjects with normal limbs resulted in an average classification performance level of 85% for the four functions.

The effects of four time-varying factors on the mean frequency of a myoelectric signal.

Daily activities involve dynamic muscle contractions that yield nonstationary myoelectric signals (MESs). The purpose of this work was to determine the individual effects of four time-varying factors (the number and firing rate of active motor units, muscle force and joint angle) on the mean frequency of a MES. Previous theoretical and experimental work revealed that although changes in the number and firing rate of active motor units contribute to the nonstationarities of the signal, they do not significantly affect the mean frequency. In the experimental work, 12 subjects performed 25 static contractions, one for each force (20, 30, 40, 50 and 60% of maximum voluntary contraction) and elbow joint angle (50, 70, 90, 110 and 130 degrees extension) combination. A MES was recorded from the surface of the biceps brachii during each contraction. The results indicated that muscle force only weakly affects the mean frequency. Also shown was that alteration in muscle geometry resulting from changes in elbow joint angle does significantly affect the mean frequency. Knowing this is important for the assessment of muscle fatigue during dynamic contractions.

Non-stationary myoelectric signals and muscle fatigue.

A mathematical derivation for the mean frequency of a myoelectric signal (MES) is provided based on an amplitude modulation model for non-stationary MES. With this derivation, it is shown that mean frequency estimates of stationary and non-stationary myoelectric signals theoretically are not significantly different in a physiologically practical context. While this prediction is confirmed via a computer simulation, it is refuted with empirical evidence. Regardless, it is shown in a final study that mean frequency is capable of tracking a downward shift in the power spectrum with fatigue even in non-stationary myoelectric signals.

Myoelectric signal analysis using neural networks.

It is shown that the capacity of a discrete Hopfield network for functional minimization allows it to extract the time-series parameters from a myoelectric signal (MES) at a faster rate than the previously used SLS algorithm. With a two-dimensional signal space consisting of one of the parameters and the signal power, a two-layer perceptron trained using back-propagation has been used to classify MES signals from different types of muscular contractions. The results suggest that neural networks may be suitable for MES analysis tasks and that further research in this direction is warranted.